A typical implantable medical device runs a classic “dual chamber” operating mode in which the device monitors the ventricular activity after an atrial event that is either spontaneous (i.e., P wave detection of an atrial depolarization) or stimulated (i.e., application of an A pulse of atrial pacing). After detecting an atrial event, the device starts to count a delay period referred to as “atrio-ventricular delay” (AVD). If no spontaneous ventricular activity (R wave detection of a ventricular depolarization) is detected at the expiry of an AVD, the device triggers stimulation of a ventricle (either left or right ventricle or both) by applying an electrical pulse for ventricular pacing.
The settings of the device are regularly reassessed to adjust stimulation parameters if necessary. The configuration and parameter settings for the stimulation therapy are appropriately modified as the patient's clinical status evolves over time.
The standard technique for adjusting stimulation parameters including the AVD starts with the estimation of the characteristic delays of the systole by echocardiography, especially the delay of opening of the aortic valve. However, this adjustment procedure should be implemented in hospitals and by qualified personnel. The procedure is long and costly, and thus cannot be applied as often as it would be useful or necessary without interfering with the patient's daily life despite the beneficial effects of the stimulation therapy.
A “multisite” implantable device that has more than two stimulation sites makes the echocardiographic assessment even more difficult because it requires testing several successive pacing configurations (e.g., selecting different sites and/or sequencing different stimulation pulses applied to the selected sites), and determining an optimal AVD for each of these configurations. For these reasons, a large number of combinations is tested rendering the procedure lengthy and difficult to manage, thus excluding it from being a routine operation.
These implantable devices ensure a joint and permanent pacing of the left and right ventricles to resynchronize them by a technique known as Cardiac Resynchronization Therapy (“CRT”) or Bi-Ventricular Pacing (“BVP”). These particular devices are commonly referred to as CRT pacemakers or CRT devices. In addition to setting an appropriate AVD, these devices optimize a delay called interventricular delay (“VVD”). The VVD is used to separate the respective moments of stimulation of the left and the right ventricles. The VVD is adjusted to resynchronize the contraction of the ventricles with a fine optimization of the patient's hemodynamic status. The search for an optimum pair (or couple) of {AVD, VVD} can therefore be very long, because it requires multiple scans and tests of the AVD for various potential values of VVD.
EP2 070 562 A1 and its counterpart US Patent Publication 2009/0157134 (both assigned to Sorin CRM S.A.S, previously known as ELA Medical) describe a technique for testing a biventricular stimulation device by successive scans a plurality of stimulation configurations.
There remains, however, a need for a technique to evaluate in a simple, rapid, automated, and precise procedure the impact of different stimulation parameters, including the AVD, so as to optimize the patient's hemodynamic status.
One automated method for optimizing the AVD is described in the article by J M Dupuis et al.: Programming Optimal atrioventricular delay in Dual Chamber Pacing Using Peak endocardial Acceleration: Comparison with a Standard Echocardiographic Procedure, PACE 2003; 26: [Pt II], 210-213. This technique involves scanning the AVD in a given stimulation configuration and tracing a characteristic value of the peak of endocardial acceleration (“PEA”) according to the AVD. The optimal value of the AVD is considered to be the inflection point of the characteristics, i.e., the point corresponding to the maximum duration of ventricular filling without truncating the A wave (i.e., the minimum delay between the closing of the mitral valve and the beginning of the QRS complex).
Although the corresponding algorithm gives satisfactory results, it requires several minutes, especially in case of a multisite device or a CRT device that requires multiple scans of AVD for various values of other parameters that are tested separately (including the VVD) to select an optimal pair of {AVD, VVD}.
Another optimization technique, which is much faster, and thus can be implemented in real time, is described in WO 2006/090397 A2 and WO 2006/126185 A2. The optimization algorithm described therein uses a spike-type neural network to identify the maximum of a hemodynamic function (e.g., stroke volume). The spike neural network, however, requires a dedicated processor, thus redesigning of the device demanding higher power consumption. A software implementation of the optimization algorithm is possible, but it requires extra computing resource that is unattainable in an ultra-low power consumption microcontroller that is adequate for use in an implantable medical device.
WO 2008/010220 describes yet another technique, in which a spike neural processor is combined with a reinforced learning algorithm (e.g., Q-learning), which learns and associates the cardiac conditions of the patient with the optimal delays. The Q-learning algorithm offers improved immunity to noise and increases the speed of convergence in searching optimal parameters. However, in order to achieve the desired performance, additional hardware resource is required, including a microprocessor in addition to the spike neural processor, which incurs extra cost, higher power consumption, and an increase spatial requirement for the implantable device.